README.md

metadata

base_model: nvidia/Llama-3.1-Nemotron-70B-Reward-HF
datasets:
  - nvidia/HelpSteer2
language:
  - en
license: llama3.1
tags:
  - nvidia
  - llama3.1
  - reward model
  - mlx
inference: false
fine-tuning: false

mlx-community/nvidia-Llama-3.1-Nemotron-70B-Reward-HF-AQ41

The Model mlx-community/nvidia-Llama-3.1-Nemotron-70B-Reward-HF-AQ41 was converted to MLX format from nvidia/Llama-3.1-Nemotron-70B-Reward-HF using mlx-lm version 0.18.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/nvidia-Llama-3.1-Nemotron-70B-Reward-HF-AQ41")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)